Complex non-invasive cardiac interventional procedures, such as, cardiac catheter ablation, carotid artery stenting, and percutaneous coronary interventions for chronic total occlusions, are routinely guided by X-ray coronary angiography that provides information about the motion and morphology of the coronary arteries under operation. Although it depends on the interventional procedure, generally, a medical professional inserts an appropriate therapeutic catheter into a blood vessel of the subject patient and advances the catheter to the target site within the vascular tree to perform actions such as deploying stents or ablating tissue. Integrating pre-operative plans into the interventional surgery suite of tasks and applications is a common way to assist the surgeon and other medical professionals during such tasks. So for example, a 3D coronary tree image around the target site can be reconstructed from a preparatory, pre-operative computed tomography (CT) scan of the patient and then overlaid on the intra-operative 2D X-ray fluoroscopy images (i.e., angiograms) to establish one image coordinate system from the different data (this is more fully described in an article by M. A. Gülsün and H. Tek, “Robust Vessel Tree Modeling”, MICCAL, Volume 5241 of LNCS (2008), pages 602-611).
This alignment or registration of pre-operative volumetric image datasets with intra-operative images is a well-known technique to help overcome some of the drawbacks of X-ray imaging but it is a computationally demanding and challenging task. Maintaining up-to-date information during the interventional procedure requires continuous re-adjustment of the registration due to cardiac motions (movement of the heart due to the cardiac cycle), bulk patient movements, and respiratory motions (respiratory-induced movement of the heart). Such re-registrations are challenging because of the low quality and lack of soft tissue contrast in intra-operative 2-D X-ray fluoroscopic images. Further, re-registrations are also computationally expensive.
Different techniques have been used or proposed to overcome the problem of cardiac and respiratory motion errors in cardiac imaging. Errors due to cardiac motion can be partly avoided using ECG gating techniques. These are techniques in which image acquisition is triggered by a start pulse derived from an ECG taken from the patient during imaging. Since the human heart rate is usually no slower than 60 beats per minute, ECG gating results in a frame rate for image acquisition of about one frame per second. Similar approaches for respiratory gating in cardiac MR imaging have been investigated, usually using displacement transducers to estimate the breathing phase (this is more fully described in an article by Ehman, et al., “Magnetic resonance imaging with respiratory gating: techniques and advantages”, AJR Am J Roentgenol 143(6) (December 1984), pages 1175-1182). However, due to the relatively long breathing cycle of several seconds, the resulting imaging frequency of respiratory-gated angiograms is significantly reduced and is not practical for interventional applications.
Another technique for reducing respiratory motion is the breath-hold technique which requires cooperation on the part the patient (this is more fully described in an article by M. R. Paling and J. R. Brookeman, “Respiration artifacts in MR imaging: reduction by breath holding”, J Comput Assist Tomogr 10(6) (1986), pages 1080-1082). Even if this method successfully reduces breathing motion by relatively simple and natural means, it is significantly restricted by the patient's limited ability to perform a supervised breath hold during the treatment (as reported in an article by G. S. Mageras and E. Yorke, “Deep inspiration breath hold and respiratory gating strategies for reducing organ motion in radiation treatment”, Semin Radiat Oncol 14(1) (January 2004), pages 65-75).
A third class of techniques addresses the problem of respiratory motion correction by incorporating suitable motion correction models in the imaging from the pre-operative volumetric image datasets (this is described in one article by D. Manke, et al., “Model evaluation and calibration for prospective respiratory motion correction in coronary mr angiography based on 3-d image registration”, Medical Imaging, IEEE Trans on 21(9) (September 2002), pages 132-1141) and in another article by A. P. King, et al., “A technique for respiratory motion correction in image guided cardiac catheterisation procedures”, Medical Imaging 6918(1) (2008), page 691816). The main drawback of these approaches is that they require manual landmark selection for diaphragm tracking. In another series of studies, a motion model algorithm relying on a parametric 3D+time coronary model has been developed to correct the X-ray images for both cardiac and respiratory motions (more fully described in one article by G. Shechter, et al., “Respiratory motion of the heart from free breathing coronary angiograms”, Medical Imaging, IEEE Trans. on 23(8) (August 2004), pages 1046-1056) and by another article by G. Shechter, et al., “Prospective motion correction of x-ray images for coronary interventions”, Medical Imaging, IEEE Trans. on 24(4) (April 2005), pages 441-450). However, the motion correction relies on the quality of the estimated motion model and does not consider any further information of the angiograms.
Studies on the respiratory motion of the heart have concluded that this kind of motion has a patient-specific profile (reported in one article by K. McLeish, et al., “A study of the motion and deformation of the heart due to respiration”, Medical Imaging, IEEE Trans. on 21(9) (September 2002), pages 1142-1150 and in another article by G. Shechter, et al., “Displacement and velocity of the coronary arteries: cardiac and respiratory motion”, Medical Imaging, IEEE Trans. on 25(3) (March 2006), pages 369-375). Even though it is more complex than just 3D translation, the motion profile is restricted in terms of 3D rotation and translation. It would be advantageous to utilize this patient-specific motion profile in developing a motion correction model for respiratory motion compensation in cardiac imaging.